Use of autonomous audio recordings for the rapid inventory of birds in the white‐sand forests of the Peruvian Amazon

White‐sand forests are patchily distributed ecosystems covering just 5% of Amazonia that host many specialist species of birds not found elsewhere, and these forests are threatened due to their small size and human exploitation of sand for construction projects. As a result, many species of birds that are white‐sand specialists are at risk of extinction, and immediate conservation action is paramount for their survival. Our objective was to evaluate current survey methods and determine the relative effect of the size of patches of these forests on the presence or absence of white‐sand specialists. Using point counts and autonomous recorders, we surveyed avian assemblages occupying patches of white‐sand forest in the Peruvian Amazon in April 2018. Overall, we detected 126 species, including 21 white‐sand forest specialists. We detected significantly more species of birds per survey point with autonomous recorders than point counts. We also found a negative relationship between avian species richness and distance from the edge of patches of white‐sand forest, but a significant, positive relationship when only counting white‐sand specialists. Although we detected more species with autonomous recorders, point counts were more effective for detecting canopy‐dwelling passerines. Therefore, we recommend that investigators conducting surveys for rare and patchily distributed species in the tropics use a mixed‐method approach that incorporates both autonomous recorders and visual observation. Finally, our results suggest that conserving large, continuous patches of white‐sand forest may increase the likelihood of survival of species of birds that are white‐sand specialists.     

Mechano- and pH-sensing convergence on Ca2+-mobilising proteins – A recipe for cancer?

Alterations in the (bio)chemical and physical microenvironment of cells accompany and often promote disease formation and progression. This is particularly well established for solid cancers, which are typically stiffer than the healthy tissue in which they arise, and often display profound acidification of their interstitial fluid. Cell surface receptors can sense changes in the mechanical and (bio)chemical properties of the surrounding extracellular matrix and fluid, and signalling through these receptors is thought to play a key role in disease development and advancement. This review will look at ion channels and G protein coupled receptors that are activated by mechanical cues and extracellular acidosis, and stimulation of which results in increases in intracellular Ca²⁺ concentrations. Cellular Ca²⁺ levels are dysregulated in cancer as well as cancer-associated cells, and mechano- and proton-sensing proteins likely contribute to these aberrant intracellular Ca²⁺ signals, making them attractive targets for therapeutic intervention.     

Monitoring an invasive perennial at the landscape scale with remote sensing

Summary   Worldwide, invasive weeds threaten agricultural, natural and urban ecosystems. In Australia's agricultural and grazing regions, invasive species often establish across extensive areas where weed management is hampered by an inability to detect the location and timing of an outbreak. In these vast landscapes, an effective detection and monitoring system is required to delineate the extent of the invasion and identify spatial and temporal factors associated with weed establishment and thickening. In this study, we utilize a time series of remote sensing imagery to detect the spatial and temporal patterns of Prickly Acacia ( Acacia nilotica ) invasion in the Mitchell grass plains of North Queensland. We develop a spectral index from Landsat images which is applied to images from 1989 to 2004, in combination with a classification mask, to identify locations and monitor changes in Prickly Acacia density across 29 000 km² of Mitchell grass plains. The approach identified spectral and temporal signatures consistent with Prickly Acacia infestation on 1.9% of this landscape. Field checking of results confirmed presence of the weed in previously unrecorded locations. The approach may be used to evaluate future spread, or outcomes of management strategies for Prickly Acacia in this landscape and could be employed to detect and monitor invasions in other extensive landscapes.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Isomeric lipid signatures reveal compartmentalized fatty acid metabolism in cancer

The cellular energy and biomass demands of cancer drive a complex dynamic between uptake of extracellular FAs and their de novo synthesis. Given that oxidation of de novo synthesized FAs for energy would result in net-energy loss, there is an implication that FAs from these two sources must have distinct metabolic fates; however, hitherto, all FAs have been considered part of a common pool. To probe potential metabolic partitioning of cellular FAs, cancer cells were supplemented with stable isotope-labeled FAs. Structural analysis of the resulting glycerophospholipids revealed that labeled FAs from uptake were largely incorporated to canonical (sn-) positions on the glycerol backbone. Surprisingly, labeled FA uptake also disrupted canonical isomer patterns of the unlabeled lipidome and induced repartitioning of n-3 and n-6 PUFAs into glycerophospholipid classes. These structural changes support the existence of differences in the metabolic fates of FAs derived from uptake or de novo sources and demonstrate unique signaling and remodeling behaviors usually hidden from conventional lipidomics.     

Anti-biofilm and anti-adherence properties of novel cyclic dipeptides against oral pathogens

Microorganisms embedded in a biofilm are significantly more resistant to antimicrobial agents and the defences of the human immune system, than their planktonic counterpart. Consequently, compounds that can inhibit biofilm formation are of great interest for novel therapeutics. In this study, a screening approach was used to identify novel cyclic dipeptides that have anti-biofilm activity against oral pathogens. Five new active compounds were identified that prevent biofilm formation by the cariogenic bacterium Streptococcus mutans and the pathogenic fungus Candida albicans. These compounds also inhibit the adherence of microorganisms to a hydroxylapatite surface. Further investigations were conducted on these compounds to establish the structure–activity relationship, and it was deduced that the common cleft pattern is required for these molecules to act effectively against biofilms.     

Evaluation of four-dimensional cone beam computed tomography ventilation images acquired with two different linear accelerators at various gantry speeds using a deformable lung phantom

We evaluated four-dimensional cone beam computed tomography (4D-CBCT) ventilation images (VI_CBCT) acquired with two different linear accelerator systems at various gantry speeds using a deformable lung phantom.The 4D-CT and 4D-CBCT scans were performed using a computed tomography (CT) scanner, an X-ray volume imaging system (Elekta XVI) mounted in Versa HD, and an On-Board Imager (OBI) system mounted in TrueBeam. Intensity-based deformable image registration (DIR) was performed between peak-exhale and peak-inhale images. VI_CBCT- and 4D-CT-based ventilation images (VI_CT) were derived by DIR using two metrics: one based on the Jacobian determinant and one on changes in the Hounsfield unit (HU). Three different DIR regularization values (λ) were used for VI_CBCT. Correlations between the VI_CBCT and VI_CT values were evaluated using voxel-wise Spearman’s rank correlation coefficient (r).In case of both metrics, the Jacobian-based VI_CBCT with a gantry speed of 0.6 deg/sec in Versa HD showed the highest correlation for all the gantry speeds (e.g., λ = 0.05 and r = 0.68). Thus, the r value of the Jacobian-based VI_CBCT was greater or equal to that of the HU-based VI_CBCT. In addition, the ventilation accuracy of VI_CBCT increased at low gantry speeds.Thus, the image quality of VI_CBCT was affected by the change in gantry speed in both the imaging systems. Additionally, DIR regularization considerably influenced VI_CBCT in both the imaging systems. Our results have the potential to assist in designing CBCT protocols, incorporating VI_CBCT imaging into the functional avoidance planning process.     

Shewanella biofilm development and engineering for environmental and bioenergy applications

The genus Shewanella comprises about 70 species of Gram-negative, facultative anaerobic bacteria inhabiting various environments, which have shown great potential in various biotechnological applications ranging from environmental bioremediation, metal(loid) recovery and material synthesis to bioenergy generation. Most environmental and energy applications of Shewanella involve the biofilm mode of growth on surfaces of solid minerals or electrodes. In this article, we first provide an overview of Shewanella biofilm biology with the focus on biofilm dynamics, biofilm matrix, and key signalling systems involved in Shewanella biofilm development. Then we review strategies recently exploited to engineer Shewanella biofilms to improve biofilm-mediated bioprocesses.